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Research paper | Open Access

Ontology enhancement using crowdsourcing: a conceptual architecture

School of Computing and Informatics, College of Biological and Physical Sciences, University of Nairobi, Nairobi, Kenya
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Abstract

Purpose

This paper aims to investigate the use of crowdsourcing in the enhancement of an ontology of taxonomic knowledge. The paper proposes a conceptual architecture for the incorporation of crowdsourcing into the creation of ontologies.

Design/methodology/approach

The research adopted the design science research approach characterised by cycles of “build” and “evaluate” until a refined artefact was established.

Findings

Data from a case of a fruit fly platform demonstrates that online crowds can contribute to ontology enhancement if engaged in a structured manner that feeds into a defined ontology model.

Research limitations/implications

The research contributes an architecture to the crowdsourcing body knowledge. The research also makes a methodological contribution for the development of ontologies using crowdsourcing.

Practical implications

Creating ontologies is a demanding task and most ontologies are not exhaustive on the targeted domain knowledge. The proposed architecture provides a guiding structure for the engagement of online crowds in the creation and enhancement of domain ontologies. The research uses a case of taxonomic knowledge ontology.

Originality/value

Crowdsourcing for creation and enhancement of ontologies by non-experts is novel and presents opportunity to build and refine ontologies for different domains by engaging online crowds. The process of ontology creation is also prone to errors and engaging crowds presents opportunity for corrections and enhancements.

References

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International Journal of Crowd Science
Pages 231-243

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Cite this article:
Kiptoo CC. Ontology enhancement using crowdsourcing: a conceptual architecture. International Journal of Crowd Science, 2020, 4(3): 231-243. https://doi.org/10.1108/IJCS-10-2019-0028

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Received: 05 October 2019
Revised: 26 February 2020
Accepted: 03 March 2020
Published: 28 April 2020
© The author(s)

Chepkoech C. Kiptoo. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode